Gene-environment interactions may be defined as a genotype's phenotypic expression being altered by the environment, e.g., the weaker effect of FTO polymorphisms on body mass index (BMI) in exercisers compared to non-exercisers. However, our preliminary analyses suggest that the phenotypic expressions of FTO polymorphisms are diminished in lean vis-a-vis overweight individuals. This suggests an alternative interpretation (Figure 1). Based on the fact that exercisers are leaner than non-exercisers, we hypothesize that physical activity affects BMI, which in turn affects FTO gene expression, rather than exercise affecting FTO gene expression directly. Our preliminary analyses of lipoproteins and BMI in 1800 subjects suggest that a genotype's phenotypic expression often increases with the quantile of the phenotype, i.e., when the value of the phenotype is high relative to its distribution in the population. We refer to this dependence as quantile- dependent penetrance. This dependence differs from the standard regression model, which assumes that the same relationship between the dependent and independent variables (e.g., phenotype vs. genotype) applies to all quantiles of the dependent variable. We propose to apply quantile regression to data available through the NHLBI Candidate-Gene Association Resource (CARe), DBGaP, and other studies to assess whether quantile-dependent penetrance applies to most other genotype-phenotype relationships. Although our preliminary analyses lacked the statistical power to assess this phenomenon for individual SNPs, its demonstration in genetic risk scores suggests that the majority of SNP effects must also be quantile dependent. We will also test whether prior assertions of gene- environment interactions are attributable to quantile-dependent penetrance, whether allowing the genotypic expression to vary with the percentile of the trait distribution significantly increases the phenotypic variances explained, and whether quantile-dependent penetrance can be extended to SNP-SNP interactions. This proposal is hypothesis driven;i.e., we hypothesize that most genotype-phenotype associations increase substantially with the percentile of the phenotype. This hypothesis is based upon the premise that the most important gene-environment interaction involves an individual's own physiological environment within which the genes are expressed. The lowest to highest percentiles of a trait's distribution represent a range of physiologic parameters, genetic make-ups, and gene-gene interactions whose presence may be essential for the genetic variant to be expressed. To our knowledge, quantile-dependent penetrance has not been proposed as a primary basis for genotype-phenotype relationships, or as an alternative to gene-environment interactions.

Public Health Relevance

We have previously shown that the effect of the genotype on a phenotype increases with the percentile of the trait distribution in genetic risk scores for total cholesterol, triglycerides, high-density lipoprotein cholesterol, and body mass index. This phenomenon was demonstrated using baseline data for a study of 1800 subjects. The purpose of this proposal is to demonstrate this phenomenon in much larger, more diverse data sets and to extend the findings to other variables. We believe that this phenomenon could be a general principal of phenotype-genotype relationships. The results could more sharply define gene-environment interactions into: a) effects of the environment on the genotype, which affects its phenotype expression, and b) effects of the environment on the phenotype which affects the penetrance of the genotype.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Exploratory/Developmental Grants (R21)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Mcallister, Kimberly A
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Lawrence Berkeley National Laboratory
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